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Medical Image Segmentation (MIS) has important ramifications for the whole of medical diagnostics. Despite the unparalleled success that various Deep Learning (DL) techniques had in image segmentation, there are setbacks with them. Generally, these setbacks arise from two of the most profound indicators of performance in DL, namely network architecture and loss function, both of which are being dealt with in the proposed method to improve the dice score on the task of MIS. To deal with the architecture part of our work, we have used Capsules that have proved to preserving more image details and indicating the spatial relationships found locally and globally, facilitating the detection of global features from the local features extracted, and hence we propose a modified capsules network for the proposed segmentation task. The loss function used in the proposed work draws inspiration from the active contours model that tends to incorporate external forces, regional information and other functions of choices in the working of segmentation extraction and treats this task as a continuous curve evolution and energy minimization problem. In our experimentation, we have used a brain tumor segmentation dataset for the performance comparison using Dice-score and mean-IoU as performance metrics and the results obtained are very optimistic. [ABSTRACT FROM AUTHOR]

• An adaptive edge indicator function is proposed to make evolution process more stable and robust to initial contours. • An adaptive sign function is proposed to enable the evolution curve to adjust evolution direction and speed. • New pre-fitting functions are computed before the iteration without online update to reduce computation cost. • A single-well potential function is provided to prevent segmentation failure and increase segmentation speed. In this study, a hybrid active contour model driven by pre-fitting energy with an adaptive edge indicator function and an adaptive sign function is proposed. The key idea of employing the pre-fitting energy is to define two pre-fitting functions to calculate mean intensities of two sub-regions separated from the selected local region based on pre-calculated median intensity of the selected local region before the curve evolves, which saves a huge amount of computation cost. In addition, a brand-new single-well potential function and its associated evolution speed function are put forward to enable evolution process to converge faster as well as more robust. Experimental outcomes indicate that this model is competent to obtain motion boundaries of different targets effectively and efficiently. Compared with traditional models and recently developed models, this model not only reduces the CPU running time and iteration number significantly as well as improves segmentation accuracies (Dice similarity coefficient (DSC) and Jaccard similarity coefficient (JSC)), but also exhibits insensitivity to initialization. [ABSTRACT FROM AUTHOR]

Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation. [ABSTRACT FROM AUTHOR]

Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented. [ABSTRACT FROM AUTHOR]

Based on the local correntropy-based K-means clustering active contour model, this paper proposes a fast two-stage segmentation method for intensity inhomogeneous images. Under our framework, the segmentation process is split into two stages. In the first stage, we preliminary segment the down-sampled images by the proposed relaxed anisotropic–isotropic local correntropy-based K-means clustering (AILCK) model, which can obtain a coarse segmentation result quickly. Subsequently, in the second stage, we further segment original images by an improved AILCK model, where we use the up-sampled coarse contour obtained by the first stage as the initialization. Following it, to obtain the global minima of energy functions fast, we incorporate a weighted difference of anisotropic and isotropic total variations into relaxed formulation of the two-stage active contour models. And then, we minimize them utilizing the difference-of-convex algorithm and the primal–dual hybrid gradient method. The experimental results on synthetic and real-world images demonstrate that the proposed method can achieve accurate segmentation results for intensity inhomogeneous images in a fast way. [ABSTRACT FROM AUTHOR]

Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation.

Edge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

Active contour models based on the level set method (LSM) are widely used in image segmentation. The major advantages of these models based on an LSM are topological flexibility and evolution robustness. However, topological flexibility is not always needed, and it could bring some negative effects for object extraction, such as extracting noise and non-objects. In this paper, a topological preservation method is proposed to constrain the evolution of contour. First, the modes of topological changes in geometric active contour models are analyzed. Second, on the basis of the modes of topological changes, the corresponding constraints are designed to keep the topology. To be specific, extracting objects with a known topology (such as k-connected objects) is viewed as a sparse representation problem under a set of basis functions. According to sparse representation, the increase or decrease of evolving contours' topology corresponds to those of the basis functions. Thus, a corresponding energy functional for topology preservation is defined based on basis functions. Finally, the proposed constraint is integrated into geometric active contour models, which is useful in extracting special objects. Experiments demonstrate that the proposed method improves the robustness of the performance of active contour models and can increase the accuracy in target object extraction.

Purpose: Minimally invasive trans-catheter aortic valve implantation (TAVI) has emerged as a treatment of choice for high-risk patients with severe aortic stenosis. However, the planning of TAVI procedures would greatly benefit from automation to speed up, secure and guide the deployment of the prosthetic valve. We propose a hybrid approach allowing the computation of relevant anatomical measurements along with an enhanced visualization. Material and methods: After an initial step of centerline detection and aorta segmentation, model-based and statistical-based methods are used in combination with 3 D active contour models to exploit the complementary aspects of these methods and automatically detect aortic leaflets and coronary ostia locations. Important anatomical measurements are then derived from these landmarks. Results: A validation on 50 patients showed good precision with respect to expert sizing for the ascending aorta diameter calculation (2.2 ± 2.1 mm), the annulus diameter (1.31 ± 0.75 mm), and both the right and left coronary ostia detection (1.96 ± 0.87 mm and 1.80 ± 0.74 mm, respectively). The visualization is enhanced thanks to the aorta and aortic root segmentation, the latter showing good agreement with manual expert delineation (Jaccard index: 0.96 ± 0.03). Conclusion: This pipeline is promising and could greatly facilitate TAVI planning. [ABSTRACT FROM AUTHOR]

Highlights • A two-phase, superpixel-based method is proposed for segmenting BUS images. • The first phase employs coarse superpixels to achieve effective tumor detection. • The second phase uses fine superpixels to enable accurate tumor segmentation. • Both phases employ edge- and region-based information to segment the superpixels. • The results show that the method enables automatic and accurate tumor segmentation. Abstract Automatic and accurate breast ultrasound (BUS) image segmentation is crucial to achieve effective ultrasound-based computer aided diagnosis (CAD) systems for breast cancer. However, segmenting the tumor in BUS images is often challenging due to several artifacts that degrade the quality of ultrasound images. In this study, a new two-phase method is proposed to enable automatic and accurate segmentation of BUS images by decomposing the image into superpixels with high boundary recall ratio and employing edge- and region-based information to outline the tumor. The first phase of the method obtains an initial outline of the tumor by decomposing the BUS image into coarse superpixels to enable effective estimation of their tumor likelihoods and employing a customized graph cuts algorithm to segment the superpixels. The segmentation of the superpixels is carried out using edge-based information that quantifies the image contour cue and region-based information that characterizes the texture content of the superpixels. In the second phase, the tumor outline is improved by decomposing the BUS image into fine superpixels that enable high boundary recall ratio and employing the customized graph cuts algorithm to segment the superpixel located around the initial tumor outline. Furthermore, an edge-based active contour model is used to smooth the tumor outline. The performance of the proposed method was evaluated using a database that includes 160 BUS images (86 benign and 74 malignant). The results indicate that the first phase of the proposed method was able to detect the tumor in all BUS images and obtain mean values of the true positive ratio (TPR), false positive ratio (FPR), false negative ratio (FNR), similarity ratio (SIR), Hausdorff error (HE), and mean absolute error (ME) equal to 91.68, 11.16, 8.32, 84.52, 17.59, and 4.67, respectively. In fact, the results obtained by the first phase of the proposed method outperform four existing BUS image segmentation algorithms. Moreover, the second phase of the proposed method was able to improve the tumor outlines of the first phase and achieve mean TPR, FPR, FNR, SIR, HE, and ME values of 96.04, 7.99, 3.96, 91.41, 11.66, and 3.65, respectively. These results suggest the feasibility of employing the proposed method, which enables automatic and accurate tumor segmentation in BUS images, to develop effective CAD systems for breast cancer. [ABSTRACT FROM AUTHOR]

In the present scenario, retinal image processing is toiling hard to get an efficient algorithm for de‐noising and segmenting the blood vessel confined inside the closed curvature boundary. On this ground, this study presents a hybrid active contour model with a novel preprocessing technique to segment the retinal blood vessel in different fundus images. Contour driven black top‐hat transformation and phase‐based binarisation method have been implemented to preserve the edge and corner details of the vessels. In the proposed work, gradient vector flow (GVF)‐based snake and balloon method are combined to achieve better accuracy over different existing active contour models. In the earlier active contour models, the snake cannot enter inside the closed curvature resulting loss of tiny blood vessels. To circumvent this problem, an inflation term Finfballoon with GVF‐based snake is incorporated together to achieve the new internal energy of snake for effective vessel segmentation. The evaluation parameters are calculated over four publically available databases: STARE, DRIVE, CHASE, and VAMPIRE. The proposed model outperforms its competitors by calculating a wide range of proven parameters to prove its robustness. The proposed method achieves an accuracy of 0.97 for DRIVE & CHASE and 0.96 for STARE & VAMPIRE datasets. [ABSTRACT FROM AUTHOR]

Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented.

For low identification ratio of attendance system of coal mine caused by difficult facial contour extraction of underground personnel, a facial contour extraction method was proposed based on active contour model (Snake model) and active shape model (ASM). Firstly, the initial contour position is estimated by ASM. Secondly, the gap between marker and real contour is narrowed after multiple iterations by Snake algorithm through defining energy function of Snake model. Finally, gray value contour of orthogonal interpolation obtained by ASM are extracted, so as to get facial contour. Experimental results show that the method can extract facial contour more accurately than traditional Snake model.

Active contour models have been widely used for image segmentation purposes. However, they may fail to delineate objects of interest depicted on images with intensity inhomogeneity. To resolve this issue, a novel image feature, termed as local edge entropy, is proposed in this study to reduce the negative impact of inhomogeneity on image segmentation. An active contour model is developed on the basis of this feature, where an edge entropy fitting (EEF) energy is defined with the combination of a redesigned regularization term. Minimizing the energy in a variational level set formulation can successfully drive the motion of an initial contour curve towards optimal object boundaries. Experiments on a number of test images demonstrate that the proposed model has the capability of handling intensity inhomogeneity with reasonable segmentation accuracy. [ABSTRACT FROM AUTHOR]

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In this paper, we deal with the problem of circle tracking across an image sequence. We propose an active contour model based on a new energy. The center and radius of the circle is optimized in each frame by looking for local minima of such energy. The energy estimation does not require edge extraction, it uses the image convolution with a Gaussian kernel and its gradient which is computed using a GPU-CUDA implementation. We propose a Newton-Raphson type algorithm to estimate a local minimum of the energy. The combination of an active contour model which does not require edge detection and a GPU-CUDA implementation provides a fast and accurate method for circle tracking. We present some experimental results on synthetic data, on real images, and on medical images in the context of aorta vessel segmentation in computed tomography (CT) images. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In active contour models (snakes), various vector force fields replacing the gradient of the original external energy in the equations of motion are a popular way to extract the object boundary. Gradient descent method is usually used to obtain the equations of motion by minimising the energy functional. However, it always suffers from local minimum in extracting complex geometries because of non‐convex functional. Gradient descent method with adaptive momentum term is proposed in this study. First, an acceleration function of evolution is defined. Then, the adaptive momentum term is obtained by calculating the product between the edge stopping function and the defined acceleration function. Finally, adaptive momentum is compatible with the snakes. The edge stopping function is used to decide the influence region of the momentum, whereas the defined acceleration function determines the magnitude of the momentum. It is used to extract the complex geometries (such as deep concavity) when adding the adaptive momentum into some snakes, such as gradient vector field or vector field convolution snakes. On the other hand, the proposed method also accelerates the rate of convergence. It can be applied to extract a single object in real images. The experimental results show that the proposed method is effective and efficient.

Skull defects result in brain infection and inadequate brain protection and pose a general danger to patient health. To avoid these situations and prevent re-injury, a prosthesis must be constructed and grafted onto the deficient region. With the development of rapid customization through additive manufacturing and 3D printing technology, skull prostheses can be fabricated accurately and efficiently prior to cranioplasty. However, an unfitted skull prosthesis made with a metal implant can cause repeated infection, potentially necessitating secondary surgery. This paper presents a method of creating suitably geometric graphics of skull defects to be applied in skull repair through active contour models. These models can be adjusted in each computed tomography slice according to the graphic features, and the curves representing the skull defect can be modeled. The generated graphics can adequately mimic the natural curvature of the complete skull. This method will enable clinical surgeons to rapidly implant customized prostheses, which is of particular importance in emergency surgery. The findings of this research can help surgeons provide patients with skull defects with treatment of the highest quality. [ABSTRACT FROM AUTHOR]

Objective of this study is to present and test a new method for metal artifact reduction (MAR) by segmenting raw CT data (sinogram). The artifact suppression technique incorporates two steps namely, metal projection segmentation in the sinogram and replacement of segmented regions by new values using an interpolation method. The proposed segmentation algorithm uses the sinogram instead of reconstructed CT slices. First, one of the best and newest region-based geometric active contour models is used to detect projection data affected by metal objects (missing projections). Then, the Hough-transform method is applied to detect all sinusoidal-like curves belonging to metal objects. Finally, a post image processing technique is used aiming to increase accuracy of the segmentation process. To provide a proof of performance, CT data of two patients with metallic teeth filling and pelvis prosthesis were included in the study as well as CT data of a phantom with metallic teeth inserts. Accuracy was determined by comparing mean, variance, mean squared error (MSE) and, peak signal to noise ratio (PSNR) as evaluation measurements of distortion in phantom images with respect to metallic teeth (original and suppressed) and without metallic teeth inserts. Quantitative results showed an average improvement of 12 dB in terms of PSNR and 517 in terms of MSE when the new MAR method was applied to remove metal artifacts. Qualitative improvement was also assessed by comparing uncorrected clinical images with artifact suppressed images. Moreover, qualitative comparison of the results of the proposed new method with the existing methods of MAR showed the superiority of the new method tested in this study. [ABSTRACT FROM AUTHOR]

Partial differential equation-based (PDE-based) methods are extensively used in image segmentation, especially in contour models. Difficulties associated with the boundaries, namely troubles with developing initialization, inadequate convergence to boundary concavities, and difficulties connected to saddle points and stationary points of active contours make the contour models suffer from a feeble performance of referring to complex geometries. The present paper is designed to take advantage of mean value theorem rather than minimizing energy function for contours. It is efficiently capable of resolving above-mentioned problems by applying this theorem to the edge map gradient vectors, which is calculated from the image. Since the contour is computed in a straightforward manner from an edge map instead of force balance equation, it varies from other contour-based image segmentation methods. To illustrate the ability of the proposed model in complex geometries and ruptures, several experiments were also provided to validate the model. The experiments’ results demonstrated that the proposed method, which is called mean value guided contour (MVGC), is capable of repositioning contours into boundary concavities and has suitable forcefulness in complex geometries. [ABSTRACT FROM AUTHOR]

Active Contour Models (ACM) have been widely used for segmentation in many computer vision applications. These models are defined by an energy functional attached to an initial curve that evolves under some constraints to extract desired objects in the image. New models are proposed, and existing techniques are investigated and improved in different domains. Among these ACM, Balloon ACM is an edge-based model that adds a normal force as constraint making the curve to have more dynamic behaviors and more effectiveness in detecting objects boundary. However, some problems have been pointed out including segmentation of complex shape and high runtime processing. In this paper, we develop a new method -called Fast Adaptive Balloon (FAB)- sufficient to segment complex shape with lower computational complexity. The proposed definition for balloon force achieves satisfactory segmentation performance compared with other ACMs using both synthetic and medical images in two dimension. The results demonstrate the accuracy and effectiveness in segmentation besides the convergence speed. [ABSTRACT FROM AUTHOR]

Intensity inhomogeneity is a well-known problem in image segmentation. In this paper, we present a new region-based active contour model for image segmentation which can handle intensity inhomogeneity problem. This model introduced a new region-based signed pressure force (SPF) function, which uses the local mean values provided by the local binary fitted (LBF) model. In addition, the proposed model utilizes a new regularization operation such as morphological opening and closing to regularize the level set function in the evolution process. Experimental results on synthetic and real images show that the proposed model gives satisfactory segmentation results as well as less sensitivity to the initial contour location and less time consuming compared to the LBF model.

Traditional active contour models are hardly convergence to object boundaries where subjective contour and complex background appear. One of the main reasons is because the weighted combination of image energy functions can produce totally different B-Spline snake behaviors. Some efforts have been focused to improve the external energy forces but also lead to higher computational cost. In this research, an approach based on the particle swarm optimization is utilized to control the snake behavior without the necessity of computing the additional information. In the proposed approach, the weighted parameters are optimized using particle swarm optimization by evaluating control points of B-Spline snake in the image energy terms. This method enables the snake to initialize the image energy coefficient automatically and in more convenient way. Experimental results demonstrate that the proposed method can improve the snake behavior.

Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer's thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (-8.38%±27.49%), while MASAC achieves the best mean DSC (0.71±0.09) and mean CE (53.92%±12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of -13.35%±11.87% and -11.15%±23.66%, a mean DSC of 0.89±0.05 and 0.71±0.09, and a mean CE of 19.19%±7.89% and 53.92%±12.65%, for PSs and CSs, respectively. Conclusions: The authors' results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors' study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation. [ABSTRACT FROM AUTHOR]

Active contour models present a robust segmentation approach, which makes efficient use of specific information about objects in the input data rather than processing all of the data. They have been widely-used in many applications, including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting road edges from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation is discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour-based methodologies, which have been developed to extract road features from LiDAR and digital imaging datasets. We present a case study in which an integrated version of active contour models is applied to extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides valuable insight into the potential of active contours for extracting roads from 3D LiDAR point cloud data.

The accurate segmentation of the optic disc (OD) offers an important cue to extract other retinal features in an automated diagnostic system, which in turn will assist ophthalmologists to track many retinopathy conditions such as glaucoma . Research contributions regarding the OD segmentation is on the rise, since the design of a robust automated system would help prevent blindness, for instance, by diagnosing glaucoma at an early stage and a condition known as ocular hypertension . Among the evaluated OD segmentation schemes, the active contour models (ACMs) have often been preferred by researchers, because ACMs are endowed with several attractive properties. To this end, we designed an OD segmentation scheme to infer how the performance of the well-known gradient vector flow (GVF) model compares with nine popular/recent ACM algorithms by supplying them with the initial OD contour derived from the circular Hough transform . The findings would hopefully equip a diagnostic system designer with an empirical support to ratify the choice of a specific model as we are bereft of such a comparative study. A dataset comprising 169 diverse retinal images was tested, and the segmentation results were assessed by a gold standard derived from the annotations of five domain experts. The segmented ODs from the GVF-based ACM coincide to a greater degree with those of the experts in 94% of the cases as predicted by the least overall Hausdorff distance value (33.49 ± 18.21). Additionally, the decrease in the segmentation error due to the suggested ACM has been confirmed to be statistically significant in view of the p values (≤1.49e−09) from the Wilcoxon signed-rank test . The mean computational time taken by the investigated approaches has also been reported. [ABSTRACT FROM AUTHOR]

Updating of existing geo-database, particularly in developing countries, is one of the important and essential issues in geospatial information systems. Numerous change detection methods have been proposed to resolve this issue. In this study, a novel approach is proposed for automatic building change detection and updating from available outdated building map and products of satellite stereo images. The proposed method consists of four steps. In the first step, preliminary building outline for a candidate building is extracted from available old polygon and DSM based on an active contour model. Then, some change detection rules are considered to find out whether or not the building has been changed. If changes are detected, the following third and fourth steps based on a hierarchical approach, run to generate precise changed building outline. The proposed method is tested and evaluated using sample dataset from Tunis City and the obtained results prove the feasibility of this algorithm for automatic building map updating using high resolution stereo satellite images. [ABSTRACT FROM AUTHOR]